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In the context of big data, we read a lot about structured versus unstructured data. So far, so good. Things get a little murky and confusing when advanced analytics—which refers to analytics for big data—joins the conversation. The confusion comes from the subtle difference between "structured data" and "structure of data," which contain almost the same words. Both concepts are key to advanced analytics, so they often come up together. In this post, I will try shed some light on this murkiness to illuminate it.
The categorization in structured, semi-structured, and unstructured data comes from the storage industry. Computers are good at chewing on large amounts of data of the same kind, like for example the readings from a meter or sensor, or the transactions on cash registers. The data is structured in the sense that each record has the same fields at the same locations, for example on an 80 or 96 column punched card, if you want a visual image. This structure is described in a schema.
Databases are optimized for storing structured data. Since each record has the same structure, the location of the i-th record on the disk is i times the record length. Therefore, it is not necessary to have a file system: a simple block storage system is all that is needed. When instead of the i-th record we need the record containing a given value in a given field, we have to scan the entire database. If this is a frequent operation in a batch step, we can accelerate it by first sorting the records by the values in this field, which allows us to use binary search, which is logarithmic instead of linear.
Because an important performance metric is the number of transactions per second, database management systems use auxiliary structures like index files and optimized query systems like SQL. In a server-based system, when we have a query, we do not want to transfer the database record by record to the client: this leads to server-based queries. When there are many clients, often the same query is issued from various clients, therefore, caching is an important mechanism to optimize the number of transactions per second.
Database management systems are very good at dealing with transactions on structured data. There are many optimization points that allow for huge performance gains, but it is a difficult art requiring highly specialized analysts.
With cloud computing, it has become very easy to quickly deploy an application. The differentiation is no longer by the optimization of the database, but in being able to collect and aggregate user data so it can be sold. This process is known as monetization and an example is click-streams. The data is to a large extent in the form of logs, but their structure is often unknown. One reason is that the schemata often change without a notification because the monetizers infer them by reverse engineering. Since the data is structured with an unknown schema, it is called semi-structured. With the Internet of Things (IoT), also known as Web of Things, Industrial Internet, etc., a massive source of semi-structured data is coming to us.
This semi-structured data is high-volume and high-velocity. This breaks traditional relational databases because data parsing and schema inference become a performance bottleneck. Also, the indexing facilities may not be able to cope with the data volume. Finally, the traditional database vendor's pricing models do not work for this kind of data. The paradigms for semi-structured data are column based storage and NoSQL (not only SQL).
The ubiquity of smartphones with their photo and video capabilities and connectedness to the cloud has brought a flood of large data files. For example, when the consumer insurance industry thought it can streamline its operations by having insured customers upload images of damages instead of keeping a large number of claim adjusters in the field, they got flooded with images. While an adjuster knows how to document a damage with a few photographs, consumers take dozens of images because they do not know what is essential.
Photographs and videos have a variety of image dimensions, resolutions, compression factors, and duration. The file sizes vary from a few dozen kilobytes to gigabytes. They cannot be stored in a database other than as a blob, for binary large object: the multimedia item is stored as a file or an object and the database just contains a file pathname or the address of an object.
In juxtaposition to conventional structured data, the storage industry talks about unstructured data.
Unstructured data can be stored and retrieved, but there is nothing else that can be done with it when we just look at it as a blob. When we looked at analytics jobs, we saw that analysts spend most of their time munging and wrangling data. This task is nothing else than structuring data because analytics is applied to structured data.
In the case of semi-structured data, this consists in reverse engineering the schema, convert dates between formats, distinguish numbers and strings from factors, and dealing correctly with missing data. In the case of unstructured data, it is about extracting the metadata by parsing the file. This can be a number of tags like the color space, or it can be a more complex data structure like the EXIF, IPTC, and XMP metadata.
A pictorial image is usually compressed with JPEG and stored in a JFIF file. The metadata in a JPEG image consists of segments beginning with a marker, the kind of the marker, and if there is a payload, the length and the payload itself. An example of a marker kind is the type (baseline or progressive) followed by width, height, number of components, and their subsampling. Other markers are the Huffman tables (HT), the quantization tables (DQT), a comment, and application-specific markers like the color space, color gamut, etc.
This illustrates that unstructured data contains a lot of structure. Once the data wrangler has extracted and munged this data, it is usually stored in R frames or in a dedicated MySQL database. These allow processing with analytics software.
Analytics is about finding even deeper structure in the data. For example, a JPEG image is first partitioned in 8×8 pixel blocks, which are each subjected through a discrete cosine transformation (DCT). Pictorially, the cosine basis (the kernels) looks like this:
The DCT transforms the data into the frequency domain, similar to the discrete Fourier transform, but in the real domain. We do this to decorrelate the data. In each of the 64 dimensions, we determine the number of bits necessary to express the values without perceptual loss, i.e., in dependence of the modulation transfer function (MTF) of the combined human visual system (HVS) and viewing device. These numbers of bits are what is stored in the discrete quantization table DQT, and we zero out the lower order bits, i.e., we quantize the values. At this point, we have not reduced the storage size of the image, but we have introduced many zeros. Now we can analyze statistically the bit patterns in the image representation and determine the optimal Huffman table, which is stored with the HT marker, and we compress the bits, reducing the storage size of the image through entropy coding.
Like we determine the optimal HT, we can also study the variation in the DCT-transformed image and optimize the DQT. Once we have implemented this code, we can use it for analytics. We can compute the energy in each of the 64 dimensions of the transformed image. As a proxy for energy, we can compute the variance and obtain a histogram with 64 abscissa points. The shape of ordinates gives us an indication of the content of the image. For example, the histogram will tell us, if an image is more likely scanned text or a landscape.
We have built a classifier, which gives us a more detailed structural view of the image.
Let us recapitulate: we transform the image to a space that gives a representation with a better decorrelation (like transforming from RGB to CIELAB). Then we perform a quantization of the values and study the histogram of the energy in the 64 dimensions. We start with a number of known images and obtain a number of histogram shapes: this is the training phase. Then we can take a new image and estimate its class by looking at its DCT histogram: we have built a classifier.
We have used the DCT. We can build a pipeline of different transformations followed by quantizations. In the training phase, we determine how well the final histogram classifies the input and propagate back the result to give a weight to each transformation in the pipeline by adjusting the quantization. In essence, this is an intuition for what happens in deep learning.
A disadvantage of machine learning is that the training phase takes a long time and if we change the kind of input we have to retrain the algorithm. For some applications, you can use your customers as free workers, like for OCR you can use the training set as captcha, which your customers will classify for free. For scientific and engineering applications you typically do not have the required millions of free workers. In the second part, we will look at unsupervised machine learning.